Latency-aware compression. Emerging compression techniques shift attention to directly prune to cut down on latency. One popular stream is Neural Architecture Search (NAS) methods [1]}, [2]}, [3]}, [4]} that adaptively adjusts the architecture of the network for a given latency requirement. They incorporate the platform constraints into the optimization process in both the architecture and parameter space to jointly optimize the model size and accuracy. Despite remarkable insights, NAS methods remain computationally expensive in general compared to their pruning counterparts.
